Igor Halperin

is this you? claim profile

Research Professor of Financial Machine Learning at NYU Tandon School of Engineering, Director of Machine Learning at Global AI, Principal COnsultant at NaN, Executive Director, Quantitave Research at JPMorgan Chase from 2003-2017, Quantitative Developer at Bloomberg LP from 1999-2003

Featured Co-authors

Matthew Dixon
3 publications
Ilya Feldshteyn
1 publication

Distributional Offline Continuous-Time Reinforcement Learning with Neural Physics-Informed PDEs (SciPhy RL for DOCTR-L)

This paper addresses distributional offline continuous-time reinforcemen...
04/02/2021 ∙ by Igor Halperin, et al. ∙ 0 ∙ share

read it
Non-Equilibrium Skewness, Market Crises, and Option Pricing: Non-Linear Langevin Model of Markets with Supersymmetry

This paper presents a tractable model of non-linear dynamics of market r...
11/03/2020 ∙ by Igor Halperin, et al. ∙ 0 ∙ share

read it
G-Learner and GIRL: Goal Based Wealth Management with Reinforcement Learning

We present a reinforcement learning approach to goal based wealth manage...
02/25/2020 ∙ by Matthew Dixon, et al. ∙ 0 ∙ share

read it
Market Self-Learning of Signals, Impact and Optimal Trading: Invisible Hand Inference with Free Energy

We present a simple model of a non-equilibrium self-organizing market wh...
05/16/2018 ∙ by Igor Halperin, et al. ∙ 0 ∙ share

read it
The QLBS Q-Learner Goes NuQLear: Fitted Q Iteration, Inverse RL, and Option Portfolios

The QLBS model is a discrete-time option hedging and pricing model that ...
01/17/2018 ∙ by Igor Halperin, et al. ∙ 0 ∙ share

read it
Inverse Reinforcement Learning for Marketing

Learning customer preferences from an observed behaviour is an important...
12/13/2017 ∙ by Igor Halperin, et al. ∙ 0 ∙ share

read it
QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds

This paper presents a discrete-time option pricing model that is rooted ...
12/13/2017 ∙ by Igor Halperin, et al. ∙ 0 ∙ share

read it
Keep It Real: Tail Probabilities of Compound Heavy-Tailed Distributions

We propose an analytical approach to the computation of tail probabiliti...
10/03/2017 ∙ by Igor Halperin, et al. ∙ 0 ∙ share

read it